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By the end of this guide, you’ll have an MCP server monitor verifying that your AI tool server starts, connects over the Model Context Protocol, and exposes the expected tools.
  • DevHelm CLI installed or an API token for REST calls
  • An API token set as DEVHELM_API_TOKEN — see Authentication
  • An MCP server package (e.g., published to npm) or a command that starts one

Create the monitor

1

Create the MCP server monitor

With the CLI, --url is the command that launches the MCP server. Arguments and environment variables aren’t settable via flags — use YAML config-as-code (config.args, config.env) or the API for the full configuration, as shown in the other tabs.
2

Verify it's running

3

Check results

Results show whether the MCP server started, connected, and listed its capabilities.

Add assertions

Verify that the MCP server exposes the tools and resources you expect:
Key MCP assertions: For the full list, see MCP Server configuration.

How it works

Each check cycle:
  1. DevHelm starts the MCP server using the configured command and args
  2. Connects via the Model Context Protocol (stdio transport)
  3. Requests the server’s tool and resource listings
  4. Evaluates assertions against the response
  5. Terminates the server process
Environment variables in env are passed to the server process. Use DevHelm secrets (${VAR} syntax) to avoid storing credentials in config files.

Next steps

MCP Server reference

Full configuration and assertion details.

First alert

Get notified when your MCP server fails.

MCP Server SDK

Use DevHelm as an MCP server in AI agents.